Lab Name

if (!require(plotly)) install.packages('plotly')
library(plotly)
if (!require(tidyverse)) install.packages('tidyverse')
library(tidyverse)
if (!require(readxl)) install.packages('readxl')
library(readxl)

Exercise 1

Insert any text here.

burden <- read_excel("Apartment List Data -- Cost Burden 2019.xlsx", skip = 1)

## New names:
## * `Total Renter Households` -> `Total Renter Households...3`
## * `Overall Cost Burden Rate` -> `Overall Cost Burden Rate...4`
## * `Moderate Cost Burden Rate` -> `Moderate Cost Burden Rate...5`
## * `Severe Cost Burden Rate` -> `Severe Cost Burden Rate...6`
## * `Number of Cost-Burdened Households` -> `Number of Cost-Burdened Households...7`
## * ...

burden <- select(burden, -12)

namelist <- c("N_Rent_Households",
              "Overall_Burden_Rate",
              "Moderate_Burden_Rate",
              "Severe_Burden_Rate",
              "N_Burden_Overall",
              "N_Burden_Moderate",
              "N_Burden_Severe",
              "Median_Rent",
              "Median_Renter_Income")

names(burden) <- c("Location",
                   "Type",
                   paste(namelist, "18", sep="_"),
                   paste(namelist, "17", sep="_"),
                   paste(namelist, "08", sep="_"),
                   paste(namelist, "change_17_18", sep="_"),
                   paste(namelist, "change_08_18", sep="_"))

myplot <- burden %>%
  filter(Type == "Metro" & Overall_Burden_Rate_18 > 0) %>%
  ggplot(aes(x=Overall_Burden_Rate_18, y=N_Burden_Overall_18)) +
    geom_point(aes(color=Overall_Burden_Rate_18)) +
    scale_y_log10(breaks = c(1000, 2000, 5000, 10000, 20000, 50000, 
                             100000, 200000, 500000, 1000000),
                labels = c("1000", "2K", "5K", "10K", "20K", "50K", 
                           "100K", "200K", "500K", "1M"),
                minor_breaks=NULL) +
    labs(title="THE U.S. CITIES WITH\nTHE BIGGEST COST BURDENS",
         subtitle=
           "Apartment List analyzed which cities have the worst income-to-rent ratios.",
         caption = "Source: Apartment List and Yahoo Finance") +
    xlab("OVERALL COST BURDEN RATE") +
    ylab("# OF COST BURDENED HOUSEHOLDS") +
    scale_color_gradient(low="yellow", high="red") +
    guides(color=FALSE) +
    theme(plot.background = element_rect(fill = "#1F0E42")) +
    theme(panel.background = element_rect(fill = "#1F0E42")) +
    theme(title = element_text(color = "white")) +
    theme(axis.text = element_text(color = "white")) +
    theme(plot.title = element_text(hjust=0.5, size = 25)) + 
    theme(plot.subtitle = element_text(hjust=0.5)) +
    scale_x_continuous(labels = scales::percent) +
    theme(panel.grid = element_line(color = "gray", size = 0.1))

## Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
## "none")` instead.

myplot

Making the Graph Interactive

ggplotly(myplot) %>%
ggplot(aes(x=Overall_Burden_Rate_18, y=N_Burden_Overall_18, ids=Location))

ggplotly(myplot)

ggplotly(myplot, tooltip = "ids") %>%
ggplot(aes(x=Overall_Burden_Rate_18, y=N_Burden_Overall_18, ids=Location,
           text=paste("Burden Rate: ",Overall_Burden_Rate_18,"%")))

ggplotly(myplot, tooltip = c("ids", "text"))

myplot <- myplot + theme(plot.title = element_text(hjust=0.5, size = 16))
ggplotly(myplot, tooltip = c("ids", "text")) %>%
  layout(title = list(text = paste("THE U.S. CITIES WITH THE BIGGEST COST BURDENS",
                                    "<br>", "<sup>",
                                    "Apartment List analyzed which cities have the worst income-to-rent ratios.",
                                    "</sup>")))